Analyses of genetic diversity and population structure of ...

RESEARCH ARTICLE

Analyses of genetic diversity and population structureof anchote (Coccinia abyssinica (Lam.) Cogn.) using newlydeveloped EST-SSR markers

Bekele Serbessa Tolera . Kifle Dagne Woldegebriel . Abel Teshome Gari .

Mulatu Geleta Dida . Kassahun Tesfaye Geletu

Received: 30 April 2020 / Accepted: 29 January 2021 / Published online: 17 February 2021

� The Author(s) 2021

Abstract Anchote (Coccinia abyssinica (Lam.)

Cogn.) is a perennial root crop belonging to Cucur-

bitaceae family. It is endemic to Ethiopia and

distributed over wide range of agro-ecologies. For

further improvement and efficient conservation of this

crop, characterization of its genetic diversity and its

pattern of distribution is a vitally important step.

Expressed sequence tags-simple sequence repeats

(EST-SSRs) markers were developed from publicly

available watermelon [Citrullus lanatus (Thunb.)

Matsum. & Nakai] ESTs in the GenBank database.

Among those novel markers, eight were polymorphic

and subsequently used for genetic diversity and

population structure analyses of 30 anchote accessions

collected from western Ethiopia. A total of 24 alleles

were obtained across the eight polymorphic loci and

30 accessions that revealed moderate level of genetic

diversity in this minor crop. Among the eight loci,

locus CA_06 was the most informative with six alleles

and polymorphic information content (PIC) of 0.76.

The accessions showed about threefold variation in

terms of genetic diversity, with expected heterozy-

gosity (He) ranging from 0.15 (accession An) to 0.44Supplementary information The online version of thisarticle (https://doi.org/10.1007/s10722-021-01132-5) containssupplementary material, which is available to authorized users.

B. Serbessa Tolera (&)

Department of Biology, Wollega University,

P. O. Box 395, Nekemte, Ethiopia

e-mail: [email protected]

K. Dagne Woldegebriel � K. Tesfaye Geletu

Department of Microbial, Cellular and Molecular

Biology, Addis Ababa University,

P. O. Box 1176, Addis Ababa, Ethiopia

e-mail: [email protected]; [email protected]

K. Tesfaye Geletu


A. Teshome Gari

The John Innes Centre, Norwich Research Park,

Norwich NR4 7UH, UK


M. Geleta Dida

Department of Plant Breeding, Swedish University of

Agricultural Sciences, Box 101, 23053 Alnarp, Sweden


K. Tesfaye Geletu

Ethiopian Biotechnology Institute, Addis Ababa, Ethiopia

123

Genet Resour Crop Evol (2021) 68:2337–2350

https://doi.org/10.1007/s10722-021-01132-5(0123456789().,-volV)( 0123456789().,-volV)

https://doi.org/10.1007/s10722-021-01132-5

http://crossmark.crossref.org/dialog/?doi=10.1007/s10722-021-01132-5&domain=pdf

https://doi.org/10.1007/s10722-021-01132-5

(accession Dg). Other accessions with higher genetic

diversity include Ar and Gu (He = 0.43 and 0.41,

respectively). Analysis of molecular variance

(AMOVA) revealed that the variation within acces-

sions and among accessions accounted for 84.7% and

15.3% of the total variation, respectively. The study

revealed low but significant population differentiation

in this crop with no clear pattern of population

structure. The EST-SSR markers developed in this

study are the first of their kind for anchote and can be

used for characterization of its wider genetic resources

for conservation and breeding purposes.

Keywords Accession � Anchote � Cocciniaabyssinica � EST-SSR � Genetic diversity � Populationstructure

Abbreviations

AMOVA Analysis of molecular variance

AR Allelic richness

EST Expressed sequence tags

FMA Frequency of major allele

SNP Single nucleotide polymorphism

SSR Simple sequence repeat

Introduction

Anchote (Coccinia abyssinica (Lam.) Cogn.) is a

perennial root crop that belongs to Cucurbitaceae

family (Edwards et al. 1995; Hora et al. 1995). It is

endemic to Ethiopia and distributed over a wide range

of agro-ecologies adapting to altitudes as low as

550 m above sea level (masl) and as high as 2800 masl

(Getahun 1973). The plant grows well in areas where

annual rainfall is between 950 and 2000 mm (Getahun

1973; Westphal 1974). This species can reproduce

both sexually and asexually through their root tubers.

Although anchote is monoecious, the main mode of

sexual reproduction is through outcrossing due to

protandry (Edwards et al. 1995). Asexually, it under-

goes an annual cycle in which its herbaceous shoots

die out and then new shoots emerge at the onset of

rainy season from its ‘‘everlasting’’ rootstock. Sowing

seeds is a preferred means of anchote cultivation as the

crop produces hundreds of seeds per fruit (Hora et al.

1995; Wondimu et al. 2014).

Anchote tuber is rich in protein and calcium with

low content of anti-nutritional factors, and hence

highly recommended as human food (Hora et al.

1995). It has also been used as traditional medicine, in

Ethiopia, to treat various illnesses such as Bone

fracture, backache, displaced joints and other diseases

such as gonorrhea, tuberculosis, and cancer (Dawit

and Estifanos 1991; Gelmesa 2010). Even though

anchote is economically, nutritionally, and medici-

nally valuable crop plant, there is limited information

regarding its genetics, breeding, best agronomic

practices and phylogeography to date. Recent studies

on its nutritional contents (Desta 2011), morpholog-

ical traits (Wondimu et al. 2014) and genetic diversity

using ISSR markers (Bekele et al. 2014) revealed key

information that can be used for its improvement.

However, these studies targeted only limited geo-

graphical areas where this crop is currently grown in

Ethiopia, and hence the sample size and geographic

coverage in previous study did not fully represent the

wider gene pool of anchote in the country.

In order to harness the nutritional values and other

benefits of this crop, quantifying its genetic diversity is

a primary step. At present, single nucleotide polymor-

phism (SNP) markers are the marker of choice as they

have genome-wide coverage and abundance in genic

regions of most studied crops. However, such molec-

ular markers require significant investment in terms of

resources and time and hence are not an immediate

option for orphan crop like anchote. Alternatively,

expressed sequence tags-simple sequence repeats

(EST-SSRs) are the second best option, as they are

quick and cheaper to develop and use (Ellis et al.

2006). EST-SSRs markers have attributes, such as

ease to develop, multi-allelic, transferable across

genera and are from expressed section of the genome.

They are also among markers of choice for QTL

analysis to identify genes driving traits of agronomic

interest (Ellis and Burke 2007). Therefore, developing

such markers for anchote, would lead to enhanced

understanding of its genome in general. In particular,

such molecular tool can guide in-situ and ex-situ

conservation efforts in Ethiopia and also play a role in

breeding new varieties. Therefore, the present study

was aimed at developing new EST-SSR markers for

anchote and uses them for assessing the genetic

diversity and population structure of anchote acces-

sions grown in Western Ethiopia where this crop is

staple food.

123

2338 Genet Resour Crop Evol (2021) 68:2337–2350

Materials and methods

Plant material

Seed samples were collected from anchote accessions

in 2013 from areas where the plant exists in nature and

where it is widely cultivated by small-scale farmers

(Fig. 1). Thirty accessions, each represented by five

individual plants, were selected based on their geo-

graphic distribution in the sampling area and their

seeds were planted at Holeta Agricultural Research

Center, Ethiopian Institute of Agricultural Research,

located 41.7 km west of Addis Ababa during the year

2013/14 crop growing season. Three weeks after

planting, young and healthy leaves were separately

sampled from each seedling representing the 30

accessions. Leaf samples were kept in a plastic zipper

containing sufficient silica gel for efficient desiccation

and stored at room temperature until DNA extraction

was conducted.

DNA extraction

Each silica gel dried leaf sample was transferred to a

2 ml eppendorf tube containing two glass beads and

was ground to fine powder using Mixer Mill MM 400

(Retsch GmbH, Germany). The DNA extraction from

the ground samples was done following protocol

described in Geleta et al. (2012). The quality and

quantity of extracted DNA was assessed by conduct-

ing agarose gel electrophoresis (1.5% (w/v) and

NanoDrop measurement. The genomic DNA

extracted from four of the samples was of poor quality

and consequently discarded. Hence, genomic DNA

Fig. 1 Map of Ethiopia showing sample collecting sites (districts) of the 30 accessions used in this study

123

Genet Resour Crop Evol (2021) 68:2337–2350 2339

from 146 individual plants representing the 30 acces-

sions was used in this study (Table 1).

EST-SSRs screening and PCR

Watermelon [Citrullus lanatus (Thunb.) Matsum. &

Nakai] expressed sequence tags were downloaded

from National Center for Biotechnology Information

(NCBI) database (https://www.ncbi.nlm.nih.gov/

nuccore). Then, WebSat (Martins et al. 2009), a web

software for microsatellite marker development, was

used to identify ESTs containing simple sequence

repeats (di, tri, tetra, penta and hexa repeats). This was

followed by using Primer3 (Rozen et al. 2000), an

online primer designing program, for designing pri-

mer-pairs targeting the SSR containing regions of the

ESTs. At the end, forty-seven primer-pairs were suc-

cessfully designed and tested to determine their

potential in amplifying the homologous DNA regions

in anchote genome. Five, randomly selected, genomic

DNA of anchote accessions were used to test these

primer-pairs.

The total reaction volume for the amplification of

the EST-SSRs was 25 ll composed of 2.5 ll of

10 9 PCR buffer (10 mM Tris–HCl pH 8.3, 50 mM

KCl, 1.25 mM MgCl2), 1.5 ll of 25 mM MgCl2,

0.3 ll of 25 mM dNTPs, 0.75 ll of 10 lM of each of

forward and reverse primers, 1.0 U (0.2 ll) of Dream

Table 1 List of 30 anchote

accessions used in this

study, their sampling

districts, and coordinates

and altitude of the sampling

sites

No Accessions code Sampling district* Coordinates Altitude (m asl)

1 Ay Ayira 9�05050.1600 N 35�23046.86 00E 1625.803

2 La Lalo Asabi 9�10014.890 N 35�28021.77 00E 1574.597

3 Ds Dale Sadi 8�53059.1200 N 35�13028.30 00E 1516.685

4 Dw Dale Wobera 8�55046.0100 N 35�03009.30 00E 1606.296

5 Sy Sayo 7�36005.6800 N 36�43031.35 00E 1843.43

6 An Anfilo 8�28019.6400 N 34�35026.55 00E 1496.568

7 Gm Gimbi 9�10015.150 0 N 35�50008.88 00E 1967.789

8 Nj Nejo 9�30008.2600 N 35�30010.22 00E 1870.862

9 Mt Metu 8�18000.0100 N 35�35000.65 00E 1703.222

10 Al Alle 8�08057.0400 N 35�32012.79 00E 1950.415

11 Hr Hurumu 8�20051.3100 N 35�41049.24 00E 1815.084

12 Yy Yayo 8�20009.4200 N 35�49020.82 00E 1598.981

13 Ch Chora 8�21039.8600 N 36�07014.56 00E 2021.129

14 De Dega 8�34006.6900 N 36�06036.03 00E 2266.798

15 Gc Gechi 8�20051.1100 N 35�51053.91 00E 1642.872

16 Dh Dhidhesa 8�04053.5500 N 36�27032.67 00E 1902.257

17 Gu Gumay 8�01000.0000 N 36�31000.00 00E 1621.536

18 Go Goma 7�51026.250 N 36�35018.16 00E 1671.523

19 Gr Gera 7�30000.140 0 N 36�04000.00 00E 2164.69

20 Sh ShebeSombo 7�30022.2500 N 36�30048.40 00E 1776.07

21 Dd Dedo 7�30058.8000 N 36�52039.12 00E 2112.569

22 Dc Decha 6�29048.3300 N 43�57058.05 00E 2150.001

23 Gg GutoGida 9�01022.3000 N 36�28024.35 00E 2242.414

24 Dg Diga 9�01032.4000 N 36�27014.53 00E 2215.896

25 Ar Arjo 8� 45018.3400 N 36�29053.66 00E 2510.638

26 Ld LekaDulecha 8�53051.9200 N 36�29011.29 00E 2161.946

27 Ss Sibu Sire 9�01058.1300 N 36�51059.67 00E 1818.742

28 Wt WayuTuka 9�04030.3700 N 36�30047.77 00E 2320.002

29 Ac AbayChomen 9�31028.0900 N 37�21004.60 00E 1980.01

30 Bt BakoTibe 9�070 05.1700 N 37�030 30.65 00E 1780.005

123


https://www.ncbi.nlm.nih.gov/nuccore

https://www.ncbi.nlm.nih.gov/nuccore

Taq DNA polymerase (Sigma, Germany), 2.5 ll of10 ng template DNA, and 16.5 ll Millipore water.

PCR amplification was performed in 96-well plates

using Thermal Cycler (S 1000TM) machine with the

following touchdown PCR conditions: Initial 95 �Cfor 3 min preheating, nine cycles of denaturation at

94 �C for 30 s, annealing at 58 �C for 45 s and primer

extension at 72 �C for 45 s. The later three steps were

repeated nine times by reducing the annealing tem-

perature by 1 �C for each subsequent touchdown

cycle. This was followed by 30 cycles of 94 �Cdenaturing for 30 s, 48 �C primer annealing for 45 s

and 72 �C primer extension for 45 s. At the end of

these cycles, additional 3 min primer extension was

done at 72 �C. To check quality and size of amplifi-

cations, sub-sample, from each PCR reactions were

loaded to ethidium bromide containing 1.5% agarose

gel and electrophoresed using 1xTAE buffer. The gels

were photographed using gel documentation system

and the fragment sizes were compared with 50 bp

DNA ladder (GeneRulerTM, Fermentas Life Sciences),

which was used as molecular size standard.

Out of the 47 EST-SSRs primer-pairs tested, 12

primer-pairs amplified a maximum of two DNA

fragments of expected size per sample and hence

were selected for use in this study. The description of

these 12 primer-pairs is provided in Table 2. The

forward primers were 50-end labeled with carboxyflu-

orescein (6-FAMTM) or hexachlorofluorescein

(HEXTM) fluorescent dyes, whereas the reverse

primers were tailed with GCTTCT to reduce

polyadenylation and improve genotyping as described

in Ballard et al. (2002). These 12 labeled primer-pairs

were used for PCR amplifications of the DNA samples

extracted from the 146 individuals representing the 30

accessions (Table 2).

The amplified products were multiplexed into

panels based on the fragment size differences and

the type of fluorescent dye of the forward primers. This

was followed by capillary gel electrophoresis using

ABI Prism� 3730xl genetic analyzer (Applied

Biosystems). The peak identification and fragment

size determination, based on the Genescan-500 LIZ

internal size standard, were done using GeneMarker

ver. 2.4.0 (SoftGenetics).

Data analyses

Various genetic diversity parameters were analyzed

using different software. Observed number of allele

(Na), effective number of alleles (Ne), percentage of

polymorphic loci (%PL), Shannon–Weaver diversity

index (I), observed heterozygosity (Ho), expected

heterozygosity (He) and gene flow (Nm) were analyzed

using POPGENE ver. 32 software (Yeh and Yang

999). DARwin ver. 6.0.112 software (2014) was used

for cluster analysis of the 30 accessions using neighbor

joining method. POWERMARKER ver. 3.25 software

(Liu and Muse 2005) was used to calculate polymor-

phic information contents (PIC) for each locus (An-

derson et al. 1993). Analysis of molecular variance

(AMOVA) was calculated using ARLEQUIN ver.

3.01 software (Excoffier et al. 2005). HP-Rare ver. 1.0

software was used to calculate allelic richness and

private allele Kalinowski (2005), allele frequency per

locus as well as gene diversity per locus and per

population. Excel add-ins (GENAIEX) ver.6.502

software Peakall and Smouse (2004) was used for

pair-wise population differentiation, and pair-wise

genetic distance.

The population structure analysis by clustering

method and determination of optimum number of

clusters (K) were conducted using STRUCTURE ver.

2.3.4 software (Pritchard et al. 2000) using admixture

model (Gilbert et al. 2012). The STRUCTURE

analysis was conducted for K = 2 to K = 25 with

burn-in period of 200,000 and number of Markov

Chain Mont Carol (MCMC) replications of 200,000,

and 10 runs at each K. Optimum K value (maximum

DK), estimation of different genetic groups, was

determined by the STRUCTURE HARVESTER soft-

ware according to Evanno et al. (2005). Cluster

alignment across the replicates was done using

CLUMPP software (Jakobsson and Rosenberg 2007)

and the population clusters were visualized by

DISTRUCT software (Rosenberg 2004).

Results

In present study twelve EST-SSR markers were

developed for anchote of which nine were di-nu-

cleotide repeats and the rest three were tri-nucleotide

repeats (Table 2). Four of these loci (CA_03, CA_04,

CA_05 and CA_12) were monomorphic (Table 2).

123


Table

2Descriptionof12EST-SSRprimer-pairsdeveloped

forCoccinia

abyssinicabased

onCitrulluslanatusESTs,andexpectedandobserved

size

(inbp)oftheiram

plified

products(alleles)

Locus/primer

nam

e

Repeat

motif

Accession

number*

Forw

ardprimer

(50 -[

30 )

Ta

(�C)

Reverse

primer

(50 -[

30 )

Ta

(�C)

EAS

OASR

Na

CA_01

(CTT)8

AI563449.1

GCAAGCTGCCTTCATTCTCT

59.7

AAAAGTGGGGGTGATAAGAGG

59.3

189

170–179

4

CA_02

(ATC)6

JG705240.1

GCGTCAAGAAATTCCCTCTC

58.9

TTGAAACTTGGAATGGATTGG

59.8

390

395–398

2

CA_03

(AT)9

JG704431.1

CAACGAAAAAGAAAACACATCC

58.6

AAGGAGAAGCTTCCAGGACA

59.0

372

397

1

CA_04

(AT)9

JG704425.1

ATCGAGGGATATGTGGTGGT

59.1

CCCCCATTTACTCAATTTCC

58.2

284

291

1

CA_05

(AT)8

JG704264.1

GAAAAAGAAAACACATCCCTTCC

60.2

GAGGCTGCTCCTCATCATCA

61.5

297

316

1

CA_06

(TC)6

JG704618.1

AGCAGCTGCAGCAGTGAAAT

61.3

GATGACCTTAAAAGATATTCTACAAGC

57.4

240

258–268

6

CA_07

(CCT)7

JG703774.1

AACCTCTCACACACAAACAAAAA

58.7

CGATGCGATTGAGAACGAA

60.9

242

234–246

3

CA_08

(TA)7

JG705018.1

TGGGTTGTTGCACAAATTGA

60.9

AAAATGAATTCCCAAGAAAGATG

58.5

300

323–339

2

CA_09

(TA)11

JG703427.1

GACAATCCAATACCCTTTACATTT

57.5

TTTGCCTTCATGTTTGCGTA

60.2

236

227–231

3

CA_10

(AG)14

JG703239.1

CCTCCAAACTCATCATTACCC

58.4

TCAATCTCAATGGACATGAAAGA

59.6

400

402–404

2

CA_11

(CT)14

JG703179.1

TTGGCTTTTGGATTCTGCTT

59.8

TAAGCCAAGACCCCTTGATG

60.1

245

225–229

2

CA-12

(AT)8

JG702160.1

CAAAATGAACACTATGACAACGAA

59.1

CATGAAGAAGATCAAGGAAAGGA

59.7

300

322–324

1

EASestimated

allele

size;OASRobserved

allele

size

range;

Naobserved

number

ofalleles

*Accessionnumber

ofwatermelonESTsusedto

developthecorrespondingSSRs

123


The other eight loci showed different levels of

polymorphism and their data was used for genetic

diversity and population structure analyses of the 30

accessions. A total of 24 alleles were identified across

the 146 samples and the eight loci (Table 3). The

observed number of alleles per locus (Na) ranged from

two to six with an average of three alleles per locus.

The effective number of allele (Ne) ranged from 1.07

(locus CA_08) to 4.70 (locus AC_06) with an average

of 1.90 (Table 4). The frequency of the alleles across

all accessions varied from 0.01 (Alleles A and D of

locus CA_01) to 0.97 (allele B of locus CA_08)

(Table 3). Locus CA_06 is the most polymorphic loci

with six alleles and polymorphic information content

(PIC) of 0.76, whereas the lowest PIC (0.06) was

recorded in locus CA_08. The highest (1.64) and

lowest (0.14) Shannon’s information index (I) were

recorded for loci CA_06 and CA_08, respectively. The

eight loci have a mean PIC and I values of 0.31 and

0.63, respectively (Table 4). Among the loci with only

two alleles, locus CA_11 is the most informative with

PIC value of 0.37. Among the eight loci, locus CA_06

and locus CA_08 are the highest and lowest values

(Table 4).

Gene diversity (He) estimated across the accessions

for each locus varied from 0.06 (locus CA_08) to 0.79

(locus CA_06) with a mean of 0.37, whereas observed

heterozygosity (Ho) varied from 0.01 (locus CA_08)

to 0.84 (locus CA_11) with a mean of 0.35 (Table 5).

Locus CA_01, CA_07, and CA_08 indicated signif-

icant inbreeding of individuals in each accession (FIS)

whereas all loci, except CA_10 and CA_11, showed

significant inbreeding of individual genotypes when

compared to random association of alleles in the whole

population (FIT). CA_02, CA_06 and CA_08 are

major contributing loci for significant population

differentiations (FST). The mean total (Ht), within

accessions (Hs) and among accessions (DST) gene

diversity were 0.37, 0.35 and 0.02, respectively. The

overall average population differentiation (FST) and

gene flow (Nm) were 0.11 and 2.06 (Table 5).

Gene diversity across the loci for each accession

varied from 0.15 (accession An) to 0.44 (accession

Dg). Other accessions with a relatively high diversity

include Ar (0.43), Gm (0.41), Gu (0.41), Ac (0.40) and

Sy (0.40) (Table 6). The average allelic richness of the

accessions varied from 1.38 (accession An) to 2.38

(accession Ar) (Table 6). Accessions with second

highest allelic richness (AR = 2.13) are Dw, Gm, Nj,

Dg, Ld, Ac, and Bt.

A minimum frequency of major allele (FMA)

observed was 0.2 for Ld accession by locus CA-10.

Table 3 Frequency distribution of different alleles of the eight polymorphic loci

Allele/Locus CA-01 CA_02 CA_06 CA_07 CA_08 CA_09 CA_10 CA_11

Allele A 0.01 0.22 0.05 0.02 0.03 0.10 0.28 0.51

Allele B 0.61 0.78 0.24 0.05 0.97 0.88 0.72 0.49

Allele C 0.37 – 0.23 0.93 – 0.02 – –

Allele D 0.01 – 0.17 – – – – –

Allele E – – 0.25 – – – – –

Allele F – – 0.05 – – – – –

The alleles are named from ‘‘A’’ to ‘‘F’’ in respective order of their allele size (small to large) within the observed allele size range

(OASR) provided in Table 1 for each locus

Table 4 Summary of genetic diversity parameters across all

accessions for the eight polymorphic loci

Locus Na Ne I PIC AFR

CA_01 4 1.96 0.75 0.39 0.01–0.61

CA_02 2 1.52 0.53 0.28 0.22–0.78

CA_06 6 4.70 1.64 0.76 0.05–0.25

CA_07 3 1.15 0.29 0.13 0.02–0.93

CA_08 2 1.07 0.14 0.06 0.03–0.97

CA_09 3 1.28 0.42 0.20 0.02–0.88

CA_10 2 1.67 0.59 0.32 0.28–0.72

CA_11 2 2.00 0.69 0.37 0.49–0.51

Mean 3 1.90 0.63 0.31

Na observed number of alleles, Ne expected number of alleles,

I Shannon diversity index, PIC polymorphic information

content, AFR allele frequency range

123


The maximum allelic richness (AR) scored was 5.0 for

both Ld and Bt accessions by the same locus, CA-06.

Average gene diversity (He) for the 30 anchote

accessions for each of the eight polymorphic loci as

well as their mean values are presented in Supple-

mentary Table 1.

The pair-wise Nei’s standard genetic distance

between the accessions varied from 0.02 (accessions

Ss vs Sh) to 0.48 (accessionsGu vsDs) with the overall

mean genetic distance of 0.13 (Supplementary

Table 2). 12.9% of the accession-pairs had genetic

distance of above 0.2 between them whereas 34.9%

pairs had genetic distance of below 0.1 between them

(data not shown). Accession Ds is the most differen-

tiated with a mean genetic distance of 0.30 from the

other accessions whereas accessions Ar and Ac are the

least differentiated with a mean genetic distance of

0.09. The analysis of pair-wise FST revealed that

12.9% accession-pairs were significantly differenti-

ated (Supplementary Table 2).

Analysis of molecular variance (AMOVA) of the

genotypic data of the 30 accessions partitioned the

Table 5 Estimates of overall heterozygosity, genetic diversity, population differentiation, F-statistics and gene flow for the eight

polymorphic loci across the 30 anchote accessions

Locus Ho He Hs Ht DST FIS FIT FST Nm

CA_01 0.35 0.49 0.48 0.49 0.01 0.23** 0.30** 0.09 2.60

CA_02 0.29 0.35 0.29 0.35 0.06 - 0.06 0.16* 0.20** 0.98

CA_06 0.71 0.79 0.72 0.80 0.07 - 0.04 0.09* 0.13** 1.65

CA_07 0.08 0.13 0.12 0.13 0.01 0.26** 0.35** 0.12 1.82

CA_08 0.01 0.06 0.07 0.10 0.02 0.90** 0.93** 0.30** 0.59

CA_09 0.19 0.22 0.21 0.22 0.01 0.06 0.15* 0.09 2.61

CA_10 0.36 0.40 0.38 0.41 0.02 0.00 0.10 0.10 2.23

CA_11 0.84 0.50 0.50 0.50 0.00 - 0.69 - 0.67 0.01 19.71

Mean 0.35 0.37 0.35 0.37 0.02 - 0.08 0.04 0.11 2.06

Ho observed heterozygosity; He expected heterozygosity; Hs gene diversity within populations; Ht total gene diversity; DST Nei’s

(1978) unbiased average gene diversity between populations; FIS, FIT, FST fixation indices; Nm Gene flow estimated from FST (Nei

1987)*P\ 0.05; **P\ 0.01

Table 6 Average

frequency of major alleles,

allelic richness, and gene

diversity of the 30 anchote

accessions

Accession FMA AR He Accession FMA AR He

Ay 0.74 2.00 0.37 Dh 0.83 1.63 0.21

La 0.78 1.88 0.30 Gu 0.70 1.88 0.41

DS 0.83 1.75 0.27 Go 0.82 1.75 0.27

Dw 0.73 2.13 0.38 Gr 0.75 1.75 0.34

Sy 0.70 2.00 0.40 Sh 0.76 1.88 0.34

An 0.90 1.38 0.15 Dd 0.82 1.75 0.26

Gm 0.75 2.13 0.41 Dc 0.83 1.63 0.25

Nj 0.71 2.13 0.39 Gg 0.74 2.00 0.37

Mt 0.78 1.88 0.32 Dg 0.66 2.13 0.44

Al 0.80 1.75 0.27 Ar 0.72 2.38 0.43

Hr 0.74 1.88 0.35 Ld 0.74 2.13 0.36

Yy 0.79 1.75 0.29 Ss 0.73 1.75 0.36

Ch 0.81 1.75 0.26 Wt 0.69 1.75 0.34

De 0.82 1.75 0.26 Ac 0.73 2.13 0.40

Gc 0.83 1.50 0.25 Bt 0.72 2.13 0.39

123


total variance into among individuals within acces-

sions and among accessions components which

accounted for 84.65% and 15.35% of the total

variance, respectively. The analysis revealed a signif-

icant population differentiation with FST value of 0.15

(P\ 0.001) (Table 7). Furthermore, the genetic

relationship between the 30 accessions was deter-

mined through Neighbor-joining cluster analysis

based on Nei’s standard genetic distance between the

accessions. The cluster analysis revealed three major

clusters, with cluster-I, II and III comprising 25, 2 and

3 accessions, respectively (Fig. 2), but fails to show

any clear association with geographic origin. The

optimal number of genetic clusters (K) revealed

through admixture model-based population structure

analysis was four (Fig. 3a). Hence, the 146 individuals

of the 30 accessions most likely originated from four

genetic populations. The analysis clearly suggested

low differentiation between the accessions, as all

accessions have alleles originated from the four

genetic populations (clusters) as shown by graphical

representation of the population genetic structure of

the 30 accessions at K = 4 (Fig. 3b).

Discussion

Anchote has a good potential to share the burden of

cereals and other crops as additional calorie source in

Ethiopia. The fact that anchote is a drought tolerant

tuber crop (Getahun 1973), makes it ideal candidate

for the current erratic climatic conditions character-

ized by frequent droughts. However, despite its

attributes in terms of economy, nutrition and resi-

lience, it has yet to receive research attention and only

semi-domesticated (Dawit and Estifanos 1991; Hora

1995; Gelmesa 2010). In the present study, a total of

12 EST-SSR markers were developed from publicly

available EST sequences of watermelon. The fact that

these markers were able to amplify expected size

fragments in anchote, opens the door of adopting other

genomic tools such as SNPs/indels developed in

related species, like watermelon, for anchote gene

pool characterization and improvement. In addition,

these newly developed EST-SSR markers can be a

vital tool for taxonomic status determination of the

species and subspecies of the genus Coccinia.

Among the newly developed EST-SSR markers,

four were monomorphic across 146 individuals

included in study, hence excluded from further

diversity analyses. These monomorphic loci may turn

out to be polymorphic if additional anchote accessions

from wider geographic areas were to be included,

because analysis of a wider gene pool of crop species

facilitates the identification of rare alleles (Kalinowski

2004). Rare alleles within genes of significant func-

tions are usually related to out-breeding species, like

anchote, where they occur in heterozygous form;

otherwise can be deleterious in their homozygote state

(Frankham 2002). Hence, further study is required to

elucidate the significance of these loci in population

genetics analyses of anchote.

The two commonly occurring types of microsatel-

lites, in flowering plant species, are di-nucleotide and

tri-nucleotide (Simko 2009; Cavagnaro et al. 2010;

Wen et al. 2010; Dillon, et al. 2014). In the present

study, di, tri, tetra, penta and hexanucleotide repeat

EST-SSRs were included in the initially targeted 47

loci although most of them were di and trinucleotide

repeat SSRs. However, only the dinucleotide and

trinucleotide repeat SSRs successful in amplifying

homologous regions in anchote (Table 1), suggesting

the higher transferability of these two groups of SSRs

than the other groups. However, changes in repeat

number of di-nucleotide repeat SSRs causes frame

shift mutation that may alter the associated protein

and/or function (Metzgar et al. 2000). Interestingly,

the highest allelic richness in the present study was

Table 7 Analysis of molecular variance (AMOVA) of the 30 anchote accessions based on data from the eight polymorphic loci

Source Degrees of freedom Sum of squares Variance components Percentage of variation FST P

Between accessions 29 79.29 0.179 va 15.35 0.15 \ 0.001

Within accessions 262 259.10 0.989 vb 84.65

Total 291 338.39 1.168 100

123


observed for locus CA_06 which is a di-nucleotide

repeat. The other polymorphic loci were also moder-

ately informative, with locus CA_01 (trinucleotide

repeat) being the second most polymorphic locus with

four alleles. In general, the maximum number of

alleles per locus (EST-SSR marker) was six, which is

comparable with what was observed in related species

like Cucumis sativus (Kong et al. 2006), Cucur-

bitaceae species (Reddy 2009) and Cucurbita pepo

(Mao et al. 2014).

The moderate genetic diversity observed in this

study indicates that strong selection pressure; probably

farmers’ intervention to improve yield and related

traits may have caused reduced polymorphism. A

similarly narrow genetic base in cultivated cultivars of

watermelon was observed when genotyped with

similar or other types of molecular markers (Wang

2011). Therefore, in the future breeding efforts,

accessions with highest genetic diversity among the

30 accessions such as Dg, Ar and Gm should be

considered for diversifying the genetic base of culti-

vated varieties and also to incorporate resilient genes

capable of withstanding different abiotic stresses. On

the contrary, An, Dh, Gc were the accessions with the

lowest genetic diversity observed, which might be

inferred to recent adaptation of anchote to the areas

where these accessions were collected (founder effect)

or recent bottleneck (Hawks et al. 2000).

The highest values of fixation indices for inbreed-

ing (FIS and FIT) were recorded for loci CA_08 and

Fig. 2 Neighbor joining tree of 30 anchote accessions constructed based on Nei’s standard genetic distance

123


CA_07 (Table 4). Theses loci have higher heterozy-

gote deficiency, because for most populations the

alleles of these loci were less than two. Such loci

should be given further research attention as they

might be linked to or located at vital genes that

minimize recombination frequency in order to prevent

loss of vital function. Contrary to these loci, locus

CA_11 was represented by excess heterozygosity

which suggest heterozygote advantage at that locus.

Multi-allelic loci are of greater value for poorly

studied crops like anchote as they can serve as

molecular barcodes for variety/accession identifica-

tion or can play a role in efficient conservation and

management of anchote germplasms in Ethiopia and

beyond. Hence, loci CA_06 and CA_01 should get

priority for these purposes, as they have higher PIC

than other loci. In this study, low but significant

differentiation was obtained between the accessions

(FST = 0.15; P\ 0.001) with 54.2% of the accession-

pairs showing significant differentiation. Similar study

using inter simple sequence (ISSR) markers revealed

higher population differentiation (FST = 0.49) (Bekele

et al. 2014). Lower population differentiation and

reduced genetic diversity are expected when EST-

SSRs are used instead of neutral markers such as ISSR,

as mutation rate in EST-SSRs is relatively low (Serra

et al. 2007). Hence, significantly differentiated acces-

sions need to be given priority both for conservation

and breeding purposes. The low population differen-

tiation is attributable to the moderate level of gene

flow between anchote populations similar with other

studies (Wickert et al. 2012). In future germplasm

collecting missions, districts from which more diverse

populations were obtained should be given priority in

order to capture further diversity. Analysis of molec-

ular variance revealed that about 85% of the total

variation accounted for the variation within acces-

sions. This is probably due to direct germplasm

exchange between farmers of different districts, and

Fig. 3 a DK plot showing its maximum value at K = 4

suggesting the optimal number of genetic clusters (populations)

of four; and b CLUMPAK plot (K = 4) of the 30 anchote

accessions based on the STRUCTURE and STRUCTURE

Harvester out puts. Each color represents a different cluster and

black vertical lines separate the accessions, and accession codes

are shown under the plot

123


through local market system as well as pollen flow

between anchote populations.

The genetic distance between populations did not

follow the geographic distance between the sampling

sites of the accessions. The neighbor-joining cluster

analysis also clearly showed complex patter of clus-

tering that is not in line with the geographic origin of

the accessions. The population structure analysis

revealed high level of admixture between the acces-

sions. The analysis also revealed not much differences

between the genotypes in terms of the likelihood of

their membership in each of the genetic populations,

indicating lack of clear population structure in

anchote. The anchote accessions included in the

present study were collected from areas where the

local communities share similar socio-economic and

food culture, which might have resulted lower than

expected differentiation/diversity between the acces-

sions and lack of clear population structure. It would

be interesting to analyze anchote germplasm repre-

senting local communities with different socioeco-

nomic and food culture in the future studies.

Conclusions

The development of novel EST-SSR markers, and

their application for the analyses of genetic diversity,

is the first of its kind in anchote. Although the number

of markers developed in this study is few, they have an

important role to play in characterization of anchote

genetic resources for conservation and breeding

purposes. The result of this study also serves as

additional evidence in supporting the across-species

transferability, within a family, of a good proportion of

EST-SSR markers. The present study revealed mod-

erate level of genetic diversity in anchote accessions

and suggested potential hotspots for conservation.

However, further research including more germ-

plasms and molecular markers is required in order to

have a clearer pattern about its population genetics as

well as for its improvement through breeding. Hence,

this study serves as a motivation for researchers to

develop genomic tools for this vital but orphan crop so

that modern breeding tools such marker assisted

selection (MAS) and genomic selection (GS) can be

applied, for accelerated delivery of high yielding and

‘‘climate smart’’ anochte varieties with wider adoption

which in turn contributes towards food and nutrition

security in Ethiopia and Eastern Africa.

Acknowledgement Wollega University partially financed

this work. We thank Addis Ababa University (Microbial,

Cellular and Molecular Biology Department) and Swedish

University of Agricultural Sciences (Department of Plant

Breeding) for their collaboration to facilitate this research;

Holeta Agricultural Research Center is acknowledged for

allowing us to grow anchote accessions used for this study at

their experimental site.

Funding This research work is financed by the Swedish

Research Council (VR) through Swedish Research Link project

grant and Addis Ababa University’s Thematic Research Project.

Availability of data and materials The authors declare that

the data supporting the findings of this study are available within

the article.

Ethics approval and consent to participate

Conflict of interest The authors declare that they have no

conflict of interest.

Not applicable.

Consent to publish We all agree to publish this work.

Consent for publication Not applicable.

Open Access This article is licensed under a Creative Com-

mons Attribution 4.0 International License, which permits use,

sharing, adaptation, distribution and reproduction in any med-

ium or format, as long as you give appropriate credit to the

original author(s) and the source, provide a link to the Creative

Commons licence, and indicate if changes were made. The

images or other third party material in this article are included in

the article’s Creative Commons licence, unless indicated

otherwise in a credit line to the material. If material is not

included in the article’s Creative Commons licence and your

intended use is not permitted by statutory regulation or exceeds

the permitted use, you will need to obtain permission directly

from the copyright holder. To view a copy of this licence, visit

http://creativecommons.org/licenses/by/4.0/.

References

Anderson J, Churchill G, Autrique J, Tanksley S, Sorrells M

(1993) Optimizing parental selection for genetic linkage

maps. Genetics 36:181–186

Ballard LW, Adams PS, Bao Y, Bartely D, Bintzler D, Kasch L,

Petukhova L, Rosato C (2002) Strategies for genotyping:

electiveness of tailing primers to increase accuracy in short

tandem repeat determinations. J Biomol Technol 13:20–29

Bekele A, Feyissa T, Tesfaye K (2014) Genetic diversity of

anchote (Coccinia abyssinica (Lam.) Cogn.) from Ethiopia

123


http://creativecommons.org/licenses/by/4.0/

as revealed by ISSR markers. Gen Res Crop Evol

61:707–719

Cavagnaro PF, Senalik DA, Yang L, Simon PW, Harkins TT,

Kodira CD, Huang S, Weng Y (2010) Genome-wide

characterization of simple sequence repeats in cucumber

(Cucumissativus L.). BMC Genom 11:1–18

Dawit A, Estifanos H (1991) Plants as a primary source of drugs

in the traditional health practices of Ethiopia. In: Engles

JMM, Hawkes JG, Worede M (eds) Plant genetic resources

of Ethiopia. Cambridge University Press, Cambridge

Desta F (2011) Phenotypic and nutritional characterization of

anchote [Coccinia abyssinica (Lam.) Cogn] accessions of

Ethiopia. M.Sc. thesis, Jimma University

Dillon NL, Innes DJ, Bally ISE,Wright CL, Devitt LC, Dietzgen

RG (2014) Expressed sequence tag-simple sequence repeat

(EST-SSR) marker resources for diversity analysis of

mango (Mangifera indica L.). Diversity 6:72–87

Edwards S, Tadesse M, Hedberg I (1995) Flora of Ethiopia and

Eritrea. National Herbarium, Addis Ababa University and

Uppsala University, Addis Ababa

Ellis JR, Burke JM (2007) EST-SSRs as a resource for popu-

lation genetic analyses. Heredity 99:125–132

Ellis JS, Knight ME, Darvill B, Goulson D (2006) Extremely

low effective population sizes, genetic structuring, and

reduced genetic diversity in a threatened umblebee species,

Bombus sylvarum (Hymenoptera: Apidae). Mol Ecol

15:4375–4386

Evanno G, Regnaut S, Goudet J (2005) Detecting the number of

clusters of individuals using the software STRUCTURE: a

simulation study. Mol Ecol 14:2611–2620

Excoffier L, Laval G, Schneider S (2005) Arlequin (version 3.0):

An integrated software package for population genetics

data analysis. Evol Biol 1:47–50

Frankham R, Ballou JD, Briscoe DA, Mclnness KH (2002)

Introduction to conservation genetics. Cambridge Univer-

sity Press, New York

Geleta M, Herrera I, Monz A, Bryngelsson T (2012) Genetic

Diversity of arabica coffee (Coffea arabica L.) in Nicar-

agua as estimated by simple sequence repeat markers. Sci

World J 2012:1–11

Gelmesa D (2010) Shifting to alternative food source: potential

to overcome Ethiopians’ malnutrition and poverty prob-

lems. In: Innovation and sustainable development in agri-

culture and food (ISDA), Montpellier-France, pp 1–10

Getahun A (1973) Developmental anatomy of tubers of anchote:

a potential dry land crop. Acta Hortic Tech Commun ISHS

33:51–64

Gilbert KJ, Andrew RL, Bock DG, Franklin MT, Kane NC,

Moore JS, Moyers BT, Renaut S, Rennison DJ, Veen T,

Vines TH (2012) Recommendations for utilizing and

reporting population genetic analyses: the reproducibility

of genetic clustering using the program STRUCTURE.

Mol Ecol 21(20):4925–4930

Hawks J, Hunley K, Lee SH, Wolpoff M (2000) Population

bottlenecks and pleistocene human evolution. Mol Biol

Evol 17(1):2–22

Hora A, Edwards S, Giday M, Tesfaye Y (1995) Anchote an

endemic tuber crop. Artistic Printing Press Enterprise,

Addis Ababa

Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster

matching and permutation program for dealing with label

switching and multimodality in analysis of population

structure. Bioinformatics 23(18):1–6

Kalinowski ST (2004) Counting alleles with rarefaction: Private

alleles and hierarchical sampling designs. Conserv Genet

5:39–543

Kalinowski ST (2005) HP-RARE 1.0: a computer program for

performing rarefaction on measures of allelic richness. Mol

Ecol Notes 5:187–189

Kong Q, Xiang C, Yu Z (2006) Development of EST-SSRs in

Cucumis sativus from sequence database. Mol Ecol Notes

6:1234–1236

Liu K, Muse SV (2005) PowerMarker: an integrated analysis

environment for genetic marker analysis. Bioinformatics

21:2128–2129

Mao W, Xu S, Ye S, Wang H, Mo GX, Gong X, Y, (2014)

Development of 19 transferable Cucurbita pepo EST-SSR

markers for the study of population structure and genetic

diversity in pumpkin (Cucurbita moschata). POJ

7(5):345–352

Martins WS, Lucas DCS, NevesBertioli KFDJ (2009) WebSat-

A web software for microsatellite marker development.

Bioinformation 3(6):282–283

Metzgar D, Bytof J, Wills C (2000) Selection against frame shift

mutations limits microsatellite expansion in coding DNA.

Genome Res 10:72–80

Peakall PR, Smouse PP (2004) GenAlEx 6.5: genetic analysis in

excel. population genetic software for teaching and

research-an update. Bioinformatics 28:2537–2539

Pritchard JK, Stephens M, Donnelly P (2000) Inference of

population structure using multilocus genotype data.

Genetics 155:945–959

Redd BU (2009) Cladistic analyses of a few members of

Cucurbitaceae using rbcL nucleotide and amino acid

sequences. Int J Biol Res 1:58–64

Rosenberg NA (2004) Distruct: a program for the graphical

display of population structure. Mol Ecol Notes 4:137–138

Rozen S, Skaletsky H (2000) Primer3 on the WWW for general

users and for biologist programmers. Meth Mol Biol

132:365–386

Serra IA, Procaaaini G, Intrieri MC, Migliaccio M, Mazzuca S,

Innocenti AM (2007) Comparison of ISSR and SSR

markers for analysis of genetic diversity in the seagrass

Posidonia oceanica. Mar Ecol Prog Ser 338:71–79

Simko I (2009) Development of EST-SSR markers for the study

of population structure in lettuce (Lactuca sativa L.). J Her100:256–262

Wang JH, Kang J, Son B, Kim K, Park Y (2011) Genetic

diversity in watermelon cultivars and related species based

on AFLPs and EST-SSRs. Not Bot Hortic Agrobiol

39(2):285–292

Wen W, Wang H, Xia Z, Zou M, Lu C, Wang W (2010)

Development of EST-SSR and genomic-SSR markers to

assess genetic diversity in Jatropha curcas L. BMC Res

Notes 3(42):1–8

Westphal E (1974) Pulses in Ethiopia, their taxonomy and

agricultural significance. Doctoral thesis, Wageningen:

Centre for Agricultural Publishing and Documentation

Wickert E, de Goes A, de Souza A, Lemos EG (2012) Genetic

diversity and population differentiation of the causal agent

of citrus black spot in Brazil. Sci World J 2012:1–14.

https://doi.org/10.1100/2012/368286

123


https://doi.org/10.1100/2012/368286

Wondimu T, Alamerew S, Ayana A, GaredewW (2014) Genetic

diversity analysis among anchote (Coccinia abyssinica)accessions in western Ethiopia. Int J Agric Res 9:149–157

Yeh FC, Yang R (1999) Popgene Version 1.31. Microsoft

Window based freeware for population genetic analysis.

University of Alberta and Tim Boyle, Centre for Interna-

tional Forestry Research, Alberta, Canada, vol 1, pp 1–28

Publisher’s Note Springer Nature remains neutral with

regard to jurisdictional claims in published maps and

institutional affiliations.

123


Analyses of genetic diversity and population structure of ...

Documents

Transcript of Analyses of genetic diversity and population structure of ...